class_docstr: GPT Language Model
code: def _set_model_config(self, config: GPTConfig) -> GPTConfig:
      type_given = config.model_type is not None
      params_given = all(
          [
              config.n_layer is not None,
              config.n_head is not None,
              config.n_embd is not None,
          ]
      )
      # assert type_given ^ params_given # exactly one of these (XOR)
      if type_given and not params_given:
          # translate from model_type to detailed configuration
          config.__dict__.update(
              {
                  # names follow the huggingface naming conventions
                  # GPT-1
                  "openai-gpt": {
                      "n_layer": 12,
                      "n_head": 12,
                      "n_embd": 768,
                  },  # 117M params
                  # GPT-2 configs
                  "gpt2": {
                      "n_layer": 12,
                      "n_head": 12,
                      "n_embd": 768,
                  },  # 124M params
                  "gpt2-medium": {
                      "n_layer": 24,
                      "n_head": 16,
                      "n_embd": 1024,
                  },  # 350M params
                  "gpt2-large": {
                      "n_layer": 36,
                      "n_head": 20,
                      "n_embd": 1280,
                  },  # 774M params
                  "gpt2-xl": {
                      "n_layer": 48,
                      "n_head": 25,
                      "n_embd": 1600,
                  },  # 1558M params
                  # Gophers
                  "gopher-44m": {
                      "n_layer": 8,
                      "n_head": 16,
                      "n_embd": 512,
                  },
                  # (there are a number more...)
                  # I made these tiny models up
                  "gpt-mini": {
                      "n_layer": 6,
                      "n_head": 6,
                      "n_embd": 192,
                  },
                  "gpt-micro": {
                      "n_layer": 4,
                      "n_head": 4,
                      "n_embd": 128,
                  },
                  "gpt-nano": {"n_layer": 3, "n_head": 3, "n_embd": 48},
              }[config.model_type]
          )
      return config
  
imports: import math
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the GPT obj's underlying member method code with example usage: obj._set_model_config(config) and returns: config